code: def load_iclaims(end_date="2018-06-24", transform=True):
      """Load iclaims dataset
  
      Returns
      -------
          pd.DataFrame
  
      Notes
      -----
      iclaims is a dataset containing the weekly initial claims for US unemployment benefits against a few related google
      trend queries (unemploy, filling and job)from Jan 2010 - June 2018. This aims to mimick the dataset from the paper
      Predicting the Present with Bayesian Structural Time Series by SCOTT and VARIAN (2014).
      Number of claims are obtained from [Federal Reserve Bank of St. Louis] while google queries are obtained through
      Google Trends API.
  
      Note that dataset is transformed by natural log before fitting in order to be fitted as a multiplicative model.
  
      https://fred.stlouisfed.org/series/ICNSA
      https://trends.google.com/trends/?geo=US
      https://finance.yahoo.com/
      """
      url = "https://raw.githubusercontent.com/uber/orbit/master/examples/data/iclaims_example.csv"
      df = pd.read_csv(
          url, parse_dates=["week"], date_format="%m/%d/%y"
      )
      df = df[df["week"] <= end_date]
  
      # standardize the regressors by mean; equivalent to subtracting mean after np.log
      regressors = [
          "trend.unemploy",
          "trend.filling",
          "trend.job",
          "sp500",
          "vix",
      ]
  
      # convert to float
      for col in regressors:
          df[col] = df[col].astype(float)
  
      if transform:
          # log transfer
          df[["claims"] + regressors] = df[
              ["claims"] + regressors
          ].apply(np.log)
          # de-mean
          df[regressors] = df[regressors] - df[regressors].apply(
              np.mean
          )
  
      return df
  
file_path: orbit/orbit/utils/dataset.py
imports: import pandas
  import numpy
  from datetime import datetime
label_desc: Use image node_id 1 for calling a global function with example usage: load_iclaims(end_date, transform) and returns: df